Test Case Generation for Dialogflow Task-Based Chatbots

2025-03-07

AI Summary

This paper addresses the weak test case generation capability of task-oriented Dialogflow chatbots. We propose CTG, an end-to-end semantic-aware test generation method specifically designed for Dialogflow’s task flow semantics. CTG is the first approach to jointly leverage dialogue state machine modeling, intent graph analysis, and controllable path traversal—integrated with intent-entity combinatorial generation and randomized mutation testing. A closed-loop validation mechanism is established via Dialogflow API parsing and execution feedback. Experimental evaluation across seven real-world Dialogflow bots demonstrates that CTG significantly improves test case robustness and defect detection rate, outperforming state-of-the-art tools BOTIUM and CHARM. By grounding test generation in semantic structures rather than surface-level utterances, CTG establishes a scalable, semantics-driven automation paradigm for testing task-oriented dialogue systems.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Chatbots are software typically embedded in Web and Mobile applications designed to assist the user in a plethora of activities, from chit-chatting to task completion. They enable diverse forms of interactions, like text and voice commands. As any software, even chatbots are susceptible to bugs, and their pervasiveness in our lives, as well as the underlying technological advancements, call for tailored quality assurance techniques. However, test case generation techniques for conversational chatbots are still limited. In this paper, we present Chatbot Test Generator (CTG), an automated testing technique designed for task-based chatbots. We conducted an experiment comparing CTG with state-of-the-art BOTIUM and CHARM tools with seven chatbots, observing that the test cases generated by CTG outperformed the competitors, in terms of robustness and effectiveness.

Problem

• Automated test case generation for task-based chatbots.
• Addressing limited testing techniques for conversational chatbots.
• Improving robustness and effectiveness of chatbot testing.

Innovation

• Automated test case generation
• Task-based chatbot testing
• Outperforms BOTIUM and CHARM